Search Results

1 - 10 of 1,384 items :

  • "data mining" x
Clear All

., & Vanthienen, J. (2000) Post-processing of association rules. At The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2000). 20 - 23 Aug 2000. Bermingham, L., & Lee, I. (2014). Spatio-temporal sequential pattern mining for tourism sciences. Procedia Computer Science , 29, 379-389. Bhandari, I., Colet, E., Parker, J., Pines, Z., Pratap, R., & Ramanujam, K. K. (1997). Advanced scout: Data mining and knowledge discovery in NBA data. Data Mining and Knowledge Discovery , 1 (1), 121-125. Bialkowski, A., Lucey, P., Carr, P., Yue, Y

References Ahmad, A., & Dey, L. (2005). A feature selection technique for classificatory analysis. Pattern Recognition Letters, 26(1), 43-56. doi: 10.1016/j.patrec.2004.08.015 Alcalá-Fdez, J., Sánchez, L., García, S., Jesus, M. J., Ventura, S., Garrell, J. M., . . . Herrera, F. (2008). KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Computing, 13(3), 307-318. doi: 10.1007/s00500-008-0323-y Aslan, B. G., & Inceoglu, M. M. (2007). A comparative study on neural network based soccer result prediction. Paper presented at the

References 1. Antons, C., E. Maltz. Expanding the Role of Institutional Research at Small Private Universities: A Case Study in Enrollment Management Using Data Mining. - New Directions for Institutional Research, Vol. 131 , 2006, 69-81. 2. Baker, R., K. Yacef. The State of Educational Data Mining in 2009: A Review and Future Visions. - Journal of Educational Data Mining, Vol. 1 , October 2009, Issue 1, 3-17. 3. Chapman, P., et al. CRISP-DM 1.0: Step-by-Step Data Mining Guide 2000. SPSS Inc. CRISPWP-0800, 2000.

R eferences [1] Appleman, Kenneth H., et al. “Collaborative internet data mining systems.” U.S. Patent No. 5,918,010. 9 Jun. 1999. [2] Blerina Lika, Kostas Kolomvatsos, Stathes Hadjiefthymiades, “Facing the cold start problem in recommender systems”, Expert Systems with Applications, volume 41, march 2014. [3] Brahima Sanou, “ITC Facts and Figures 2013”, Telecommunication Development Bureau, International Telecommunications Union (ITU), Geneva, February 2013. Retrieved 23 May 2015. [4] Chakrabarti, Soumen, et al. Data mining: know it all. Morgan Kaufmann, 2008

References 1. Baesens, B., Mues, C., Martens, D., Vanthienen, J. (2009), “50 years of data mining and OR: upcoming trends and challenges“, Journal of the Operational Research Society, Vol. 60, pp. S16-S23. 2. Bose, R. (2006), “Intelligent technologies for managing fraud and identity theft“, in Third International Conference on Information Technology: New Generations, (ITNG 2006), IEEE, pp. 446-451. 3. Chen, W. H., Hsu, S. H., Shen, H. P. (2005), “Application of SVM and ANN for intrusion detection“, Computors & Operations Research, Vol. 32 No. 10, pp. 2617-2634. 4

. - In: Proc. of 30th International Conference on Very Big Data Bases, Volume 30.VLD B Endowment, 2004, pp. 852-863. 5. Golab, L., M. T. Özsu. Issues in Data Stream Management. - ACM SIGMOD Record, Vol. 32, 2003, No 2, pp. 5-14. 6. Margahny, M. H., A. A. Mitwally. Fast Algorithm for Mining Association Rules. - In: Proc. of Conference AIML, CICC (pp. 36-40), Cairo, Egypt. 2005, pp. 19-21. 7. Han, J., J. Pei, Y. Yin et al. Mining Frequent Patterns Without Candidate Generation: A Frequent-Pattern Tree Approach. - Data Mining and Knowledge Discovery, Vol. 8, 2004, No 1, pp

References 1. Zhan g, G. L., J. S. Lei, X. H. Wu. An Improved Apriori Algorithm for Mining Association Rules. - Computer Technology and Development, Vol. 20, 2010, No 6, 84-89. 2. Ding, R. Embedded Database Technology. Xi’an, Northwest Industry University Press, 2001, 65-91. 3. Naveen Kumar, Sanjay Kumar, Abid Haleem, Pardeep Gahlot. Implementing Lean Manufacturing System: ISM Approach. - Journal of Industrial Engineering and Management, Vol. 6, 2013, No 4, 996-1012. 4. Liu, Y., C. Y. Yu, X. J. Zhan g. The Application of Embedded Database in Data Mining System

References Data Mining: Fundamentals / A. Sukovs L. Aleksejeva K. Makejeva et al. - Riga: Riga Technical University, 2007, p.130. (In Latvian). Thomassey S., Fiordaliso A. A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems , Volume 42, Issue 1, 2006, p. 408-421. Written I.H., Frank E. Data mining:practical machine learning tools and techniques - 2 nd edition. - Amsterdam etc.: Morgan Kaufman, 2005. Das G., Lin K., Manilla H., Renganathan G., Smyth P. Rule Discovery from Time Series. // In Proceedings of the 3 rd

association rules with consideration of support measure”, Expert Systems with Applications, Vol. 40, No. 16, pp. 6531–6537. 4. European Commission (2012), “EuroStat, The Community Innovation Survey 2012”, available at (15 April 2015). 5. Heinrichs, J. H., Lim J. S. (2003), “Integrating web-based data mining tools with business models for knowledge management”, Decision Support Systems, Vol. 35, No. 1, pp. 103-112. 6. Javaheri, S. F., Sepehri, M. M

References [1] ADAM, N. A.-WORTMAN, J. C.: Security-control methods for statistical databases , ACM Comput. Surv. 21 (1989), 515-556. [2] Privacy-Preserving Data Mining: Models and Algorithms (C. C. Aggarwal, P. S. Yu, eds.), Springer, New York, NY, USA, 2008. [3] ATZORI, M.-BONCHI, F.-GIANNOTTI, F.-PEDRESCHI, D.: Anonymity preservingpattern discovery , VLDB J. 17 (2008), 703-727. [4] DOMINGO-FERRER, J.-SAYGIN, Y.: Recent progress in database privacy , Data Knowl. Eng. 68 (2009), 1157-1159. [5] FAYYAD, U. M.-PIATETSKY-SHAPIRO,G.-SMYTH, P.: From data